CN118694994B - Live broadcast on-stream operation regulation and control method and system - Google Patents
Live broadcast on-stream operation regulation and control method and system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
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- H—ELECTRICITY
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- H04L47/70—Admission control; Resource allocation
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/24—Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth, upstream requests
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
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Abstract
The invention provides a live broadcast start operation regulation and control method and system, which relate to the technical field of operation regulation and control, acquire live broadcast start data, determine a main broadcasting end and a spectator end, monitor the operation data in real time and determine a monitoring target user end; dividing the acquired live broadcast function types into indexes to obtain monitoring indexes, collecting index monitoring data, comparing the index monitoring data with a preset index threshold value, and judging abnormal indexes and normal indexes according to comparison results; calculating the abnormal function coefficient of each monitoring target user terminal, calculating the abnormal function coefficient of the user terminal according to the abnormal function coefficient, comparing the abnormal function coefficients of the user terminal with each other, and determining the abnormal user terminal and the risk user terminal according to the comparison result; the invention solves the problems of the existing live broadcast system in the aspects of data utilization, real-time monitoring, function evaluation, resource allocation and the like.
Description
Technical Field
The invention provides a live broadcast on-stream operation regulation and control method and system, relates to the technical field of operation regulation and control, and particularly relates to the technical field of live broadcast on-stream operation regulation and control.
Background
With the rapid development of network technology and the wide popularization of Internet application, the live broadcast industry has come up with unprecedented development opportunities. However, while live broadcast services are being developed vigorously, many technical challenges are faced, especially in the aspects of effective utilization of live broadcast data, real-time monitoring of the running state of a user end, evaluation of the stability of live broadcast functions, intelligent allocation of network resources, and the like. Although the existing live broadcast system can acquire live broadcast data, the existing live broadcast system often lacks of deep analysis and effective utilization of the data, and a main broadcasting end and a spectator end cannot be accurately and rapidly identified, so that targeted monitoring and management are difficult to implement. Meanwhile, in the aspect of real-time monitoring of operation data of a user side, the traditional method mostly depends on manual inspection or simple threshold judgment, and has the problems of untimely monitoring, low accuracy and the like, and the continuity and stability of live broadcasting cannot be effectively ensured. In addition, the functions involved in the live broadcast process are of a wide variety, each having its own specific performance requirements and monitoring metrics. However, the existing live broadcast system often lacks detailed division of live broadcast function types and accurate definition of monitoring indexes, so that scientific basis is lacking in function stability evaluation, and potential problems are difficult to discover and solve in time.
Disclosure of Invention
The invention provides a live broadcast operation regulation and control method and system, which are used for solving the problems that the abnormal situation in the live broadcast process is difficult to accurately identify, position, regulate and the like in the prior art:
the invention provides a live broadcast on-stream operation regulation and control method and a system, wherein the method comprises the following steps:
s1, acquiring live broadcast data, determining a main broadcasting end and a spectator end according to the live broadcast data, monitoring operation data of the main broadcasting end and the spectator end in real time, and determining a monitoring target user end according to obtained user end monitoring data;
S2, dividing the acquired live broadcast function types into indexes to obtain monitoring indexes, collecting index monitoring data, comparing the index monitoring data with a preset index threshold value, and judging an abnormal index and a normal index according to a comparison result;
s3, calculating the function abnormality coefficient of each monitoring target user terminal, calculating the user terminal abnormality coefficient according to the function abnormality coefficient, comparing the user terminal abnormality coefficients with each other, and determining an abnormal user terminal and a risk user terminal according to a comparison result;
And S4, distributing network redundancy operation resources to the abnormal user side and the risk user side to obtain a distribution result.
Further, the step S1 includes:
Acquiring live broadcast data, and acquiring user side information of a live broadcast room according to the live broadcast data;
acquiring a video connection user end and a voice connection user end only from live broadcasting room user end information, wherein the video connection user end and the voice connection user end only are anchor ends;
The user end except the anchor end is a spectator end;
And monitoring the operation data of the video connection user terminal and the voice connection user terminal in real time to obtain the user terminal monitoring data of the video connection user terminal and the voice connection user terminal only and determine a monitoring target user terminal.
Further, the step S2 includes:
Acquiring live broadcast function types, and dividing indexes of each live broadcast function type according to preset index types to acquire a plurality of monitoring indexes of the live broadcast function types;
Collecting index data of each monitoring index of each anchor end and each audience end to obtain index monitoring data;
comparing the index monitoring data with a preset index threshold value to obtain an index comparison result;
And judging the normal index or the abnormal index of the corresponding monitoring index according to the index comparison result to obtain an index judgment result.
Further, the step S3 includes:
acquiring index monitoring data of abnormal indexes according to the index judgment result, and calculating the functional abnormality coefficient of each live broadcast functional category of each monitoring target user side according to the index monitoring data of each monitoring target user side;
calculating a user side abnormal coefficient of each monitoring target user side according to the plurality of function abnormal coefficients;
comparing each user side abnormal coefficient with other user side abnormal coefficients respectively to obtain a user side abnormal comparison result;
and determining an abnormal user terminal according to the user terminal comparison result.
When the abnormal coefficient of the user terminal is larger than the average value of all other abnormal coefficients of the user terminal, judging that the monitoring target user terminal corresponding to the abnormal coefficient of the user terminal is an abnormal user terminal;
and when the abnormal coefficient of the user terminal is smaller than or equal to the average value of all other abnormal coefficients of the user terminal, judging that the monitoring target user terminal corresponding to the abnormal coefficient of the user terminal is a risk user terminal.
Further, the step S4 includes:
Acquiring network redundant operation resources of a live broadcasting room, and averagely distributing 2/3 of the network redundant operation resources to all abnormal user terminals;
and uniformly distributing 1/3 of the network redundant operation resources to the risk user side.
Further, the system comprises:
The monitoring target determining module is used for acquiring live broadcast data, determining a main broadcasting end and a spectator end according to the live broadcast data, monitoring the operation data of the main broadcasting end and the spectator end in real time, and determining a monitoring target user end according to the acquired user end monitoring data;
The index monitoring analysis module is used for carrying out index division on the acquired live broadcast function types to obtain monitoring indexes, collecting index monitoring data, comparing the index monitoring data with a preset index threshold value, and judging abnormal indexes and normal indexes according to comparison results;
the user side monitoring analysis module is used for calculating the function abnormality coefficient of each monitoring target user side, calculating the user side abnormality coefficient according to the function abnormality coefficient, comparing the user side abnormality coefficients with each other, and determining an abnormal user side and a risk user side according to the comparison result;
The resource allocation module is used for allocating network redundancy operation resources to the abnormal user side and the risk user side to obtain an allocation result.
Further, the monitoring target determining module includes:
Acquiring live broadcast data, and acquiring user side information of a live broadcast room according to the live broadcast data;
acquiring a video connection user end and a voice connection user end only from live broadcasting room user end information, wherein the video connection user end and the voice connection user end only are anchor ends;
The user end except the anchor end is a spectator end;
And monitoring the operation data of the video connection user terminal and the voice connection user terminal in real time to obtain the user terminal monitoring data of the video connection user terminal and the voice connection user terminal only and determine a monitoring target user terminal.
Further, the index monitoring and analyzing module includes:
Acquiring live broadcast function types, and dividing indexes of each live broadcast function type according to preset index types to acquire a plurality of monitoring indexes of the live broadcast function types;
Collecting index data of each monitoring index of each anchor end and each audience end to obtain index monitoring data;
comparing the index monitoring data with a preset index threshold value to obtain an index comparison result;
And judging the normal index or the abnormal index of the corresponding monitoring index according to the index comparison result to obtain an index judgment result.
Further, the client monitoring and analyzing module includes:
acquiring index monitoring data of abnormal indexes according to the index judgment result, and calculating the functional abnormality coefficient of each live broadcast functional category of each monitoring target user side according to the index monitoring data of each monitoring target user side;
calculating a user side abnormal coefficient of each monitoring target user side according to the plurality of function abnormal coefficients;
comparing each user side abnormal coefficient with other user side abnormal coefficients respectively to obtain a user side abnormal comparison result;
and determining an abnormal user terminal according to the user terminal comparison result.
When the abnormal coefficient of the user terminal is larger than the average value of all other abnormal coefficients of the user terminal, judging that the monitoring target user terminal corresponding to the abnormal coefficient of the user terminal is an abnormal user terminal;
and when the abnormal coefficient of the user terminal is smaller than or equal to the average value of all other abnormal coefficients of the user terminal, judging that the monitoring target user terminal corresponding to the abnormal coefficient of the user terminal is a risk user terminal.
Further, the resource allocation module includes:
Acquiring network redundant operation resources of a live broadcasting room, and averagely distributing 2/3 of the network redundant operation resources to all abnormal user terminals;
and uniformly distributing 1/3 of the network redundant operation resources to the risk user side.
The invention has the beneficial effects that: the real-time monitoring and quick response mechanism is beneficial to timely finding and solving various abnormal problems in the live broadcast process, such as network jamming, equipment faults and the like, so that the live broadcast stability is improved. By optimizing network resources and equipment performance of the audience terminal, delay and blocking phenomena are reduced, and viewing experience of users is improved. By accurately identifying the abnormal and risk user terminals, reasonable distribution and efficient utilization of network resources are realized, and unnecessary operation cost is reduced. The large amount of monitoring data collected by the system provides powerful support for subsequent decisions, and is helpful for making more scientific and reasonable live broadcasting strategies and popularization plans.
The method has good expandability and flexibility, can be continuously upgraded and perfected along with the development of live broadcast service, and meets more demands in the future.
Drawings
FIG. 1 is a schematic diagram of a method for regulating operation of live broadcast operation;
fig. 2 is a schematic diagram of a live broadcast operation control system.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention provides a live broadcast operation regulation and control method and a system, which are used for solving the problem:
the invention provides a live broadcast on-stream operation regulation and control method and a system, wherein the method comprises the following steps:
s1, acquiring live broadcast data, determining a main broadcasting end and a spectator end according to the live broadcast data, monitoring operation data of the main broadcasting end and the spectator end in real time, and determining a monitoring target user end according to obtained user end monitoring data;
S2, dividing the acquired live broadcast function types into indexes to obtain monitoring indexes, collecting index monitoring data, comparing the index monitoring data with a preset index threshold value, and judging an abnormal index and a normal index according to a comparison result;
s3, calculating the function abnormality coefficient of each monitoring target user terminal, calculating the user terminal abnormality coefficient according to the function abnormality coefficient, comparing the user terminal abnormality coefficients with each other, and determining an abnormal user terminal and a risk user terminal according to a comparison result;
And S4, distributing network redundancy operation resources to the abnormal user side and the risk user side to obtain a distribution result.
The working principle of the technical scheme is as follows: the system firstly acquires initial data of live broadcast and on-demand, including basic information, on-demand time, anchor information and the like of the live broadcast. From this data, the system determines the anchor side (i.e., the provider of the live content) and the viewer side (i.e., the recipient of the live content). The system monitors the running data of the anchor side and the audience side in real time, including but not limited to network status, device performance, live content quality and the like. The monitoring data are collected by means of sensors, API interfaces and the like, and are subjected to preliminary processing and analysis. According to the obtained monitoring data of the user terminal, the system screens out monitoring target user terminals needing to be concerned, and the user terminals possibly become potential risk points due to network problems, insufficient equipment performance and the like. The system classifies the types of live broadcast functions, such as audio and video quality, interactive functions, payment functions and the like. For each function, corresponding monitoring indexes such as a click-through rate, delay time, interaction response speed and the like are set. The system collects real-time data of each monitoring index and compares the real-time data with a preset index threshold. Based on the comparison result, it is determined which indexes are abnormal (exceeding the threshold) or normal (within the threshold). And calculating the function abnormality coefficient of each monitoring target user terminal, wherein the coefficient is comprehensively obtained based on the abnormality degrees of a plurality of monitoring indexes. And further calculating the user side abnormal coefficients according to the functional abnormal coefficients, and comparing the user side abnormal coefficients with each other. According to the comparison result, determining which clients are abnormal clients (obvious problems exist directly) and risk clients (problems possibly occur in the future). And allocating network redundancy operation resources, such as bandwidth increase, route optimization, standby server starting and the like, to the abnormal user side and the risk user side. The distribution result aims to alleviate or eliminate the problems existing at the user side and improve the live broadcasting stability and user experience.
The technical effects of the technical scheme are as follows: the real-time monitoring and quick response mechanism is beneficial to timely finding and solving various abnormal problems in the live broadcast process, such as network jamming, equipment faults and the like, so that the live broadcast stability is improved. By optimizing network resources and equipment performance of the audience terminal, delay and blocking phenomena are reduced, and viewing experience of users is improved. By accurately identifying the abnormal and risk user terminals, reasonable distribution and efficient utilization of network resources are realized, and unnecessary operation cost is reduced. The large amount of monitoring data collected by the system provides powerful support for subsequent decisions, and is helpful for making more scientific and reasonable live broadcasting strategies and popularization plans.
The method has good expandability and flexibility, can be continuously upgraded and perfected along with the development of live broadcast service, and meets more demands in the future.
In one embodiment of the present invention, the S1 includes:
Acquiring live broadcast data, and acquiring user side information of a live broadcast room according to the live broadcast data;
acquiring a video connection user end and a voice connection user end only from live broadcasting room user end information, wherein the video connection user end and the voice connection user end only are anchor ends;
The user end except the anchor end is a spectator end;
And monitoring the operation data of the video connection user terminal and the voice connection user terminal in real time to obtain the user terminal monitoring data of the video connection user terminal and the voice connection user terminal only and determine a monitoring target user terminal. The current online user end which is in communication connection is the monitoring target user end, and the disconnected user end is removed from the monitoring target user end.
The working principle of the technical scheme is as follows: the system is started first, and is connected to a data source of the live broadcast service to acquire initial data of the live broadcast. Such data may include the ID of the live broadcast, the time of play, the unique identification of the live broadcast room, etc. Based on the live broadcast data, the system further queries or subscribes to the real-time user side information of the live broadcast room. Such information typically includes the type of user side (e.g., anchor, audience), device information, network status, etc. In the live broadcasting room user side information, the system identifies a video connection user side and a voice connection user side only. Both types of clients are categorized as anchor because they directly contribute to the live content (i.e., live). After all the anchor terminals are removed, the rest of the user terminals are automatically classified as audience terminals. These clients mainly receive live content, but do not directly affect the live content. The system monitors the running data of the video connection user end and the voice connection user end in real time. The monitored data may include network latency, bandwidth occupancy, device performance (e.g., CPU, memory usage), audio video quality, etc. Based on the real-time monitored data, the system determines which anchor is currently online and in active communication connection. These clients are considered monitoring target clients because their status directly affects the stability and quality of the live broadcast. Once a particular anchor is disconnected or communication is interrupted, the system immediately removes it from the monitoring target client list to avoid unnecessary resource consumption and error monitoring.
The technical effects of the technical scheme are as follows: through monitoring the real-time operation data of the anchor, the system can timely discover and process potential problems which possibly affect the live broadcasting stability, such as network fluctuation, insufficient equipment performance and the like, so that the overall stability of live broadcasting is improved. Monitoring the running state of the anchor end is beneficial to ensuring the continuity and high-quality transmission of live broadcast content, reducing the problems of blocking, delay and the like possibly encountered by the audience end, and improving the watching experience of users. The system dynamically adjusts the monitoring target according to the currently online and communication connection anchor terminal, avoids invalid monitoring of disconnected or invalid user terminals, and improves the utilization efficiency of system resources. The real-time monitoring mechanism enables the system to rapidly respond to the change and abnormal conditions of the anchor end, rapidly locate the problem, take corresponding solving measures and reduce the influence of faults on live broadcasting. The clear host-side and audience-side classification and real-time monitoring mechanism make the maintenance and management of the system simpler and more efficient. The administrator can intuitively understand the real-time condition of the live broadcasting room and make necessary adjustment and optimization accordingly.
In one embodiment of the present invention, the S2 includes:
Acquiring live broadcast function types, and dividing indexes of each live broadcast function type according to preset index types to acquire a plurality of monitoring indexes of the live broadcast function types;
Collecting index data of each monitoring index of each anchor end and each audience end to obtain index monitoring data;
comparing the index monitoring data with a preset index threshold value to obtain an index comparison result;
And judging the normal index or the abnormal index of the corresponding monitoring index according to the index comparison result to obtain an index judgment result.
When the index monitoring data is larger than a preset index threshold value, judging the corresponding monitoring index as an abnormal index;
and when the index monitoring data is smaller than or equal to a preset index threshold value, judging the corresponding monitoring index as a normal index.
The working principle of the technical scheme is as follows: the system first identifies all live function categories available on the live platform, such as audio and video transmission, barrage interaction, gift presentation, wheat-over interaction, etc. For each live broadcast function type, the system performs detailed index division according to a preset index type. The index categories may include performance index (such as delay, packet loss rate), quality index (such as audio/video definition, synchronicity), user behavior index (such as interaction frequency, gift gifting amount), etc. Through the above division, the system obtains a plurality of specific monitoring indexes for each live function category. The system performs real-time or periodic data acquisition on each monitoring index of each anchor side and each audience side. The data collection means may include reading the server log directly, retrieving data through an API interface, deploying a monitoring probe at the client, etc. And after preliminary processing and cleaning, the acquired data form index monitoring data. The system compares the collected index monitoring data with a preset index threshold. The thresholds are preset according to service requirements and system performance requirements and are used for judging whether the monitoring index is in a normal range or not. The comparison result is divided into two cases: when the index monitoring data is larger than a preset index threshold value, the system judges the monitoring index as an abnormal index; and when the index monitoring data is smaller than or equal to a preset index threshold value, judging the index to be a normal index. The judging result is used for subsequent scenes such as exception handling, performance optimization, user experience improvement and the like.
The technical effects of the technical scheme are as follows: by dividing detailed indexes of the live broadcast function types and collecting real-time monitoring data, the system can accurately monitor various performance indexes and quality indexes in the live broadcast process, so that the system can quickly respond and solve potential problems. Through timely discovery and processing of abnormal indexes, the system can optimize the quality of live audio and video, reduce the problems of delay, packet loss and the like, and promote the viewing experience of users. Monitoring the user behavior index is helpful for the system to know the preference and the demand of the user, so that more personalized and accurate service is provided, and the user experience is further improved. The real-time monitoring and abnormality judging mechanism is beneficial to timely finding and processing potential system faults and potential safety hazards by the system, and stability and reliability of the system are enhanced. The large amount of monitoring data collected by the system provides powerful support for subsequent decisions. Through data analysis, enterprises can know information such as development conditions of live broadcast services, user behavior characteristics and the like, so that more scientific and reasonable service strategies and development planning are formulated. The whole monitoring and judging process realizes automation and intellectualization, reduces the possibility of manual intervention and erroneous judgment, and improves the working efficiency and accuracy.
In one embodiment of the present invention, the S3 includes:
acquiring index monitoring data of abnormal indexes according to the index judgment result, and calculating the functional abnormality coefficient of each live broadcast functional category of each monitoring target user side according to the index monitoring data of each monitoring target user side;
The calculation formula of the functional abnormality coefficient is as follows:
wherein G nyx is a functional abnormality coefficient, j is the total number of indexes of the live broadcast functional category, L yci is the acquired abnormal data volume of the ith index, L cji is the data acquisition volume of the ith index, L zbi is the index data total amount measurement of the ith index, Q i is the weight coefficient of the ith index, and the weight coefficient has a value range of 0-1 and does not comprise 0.
Calculating a user side abnormal coefficient of each monitoring target user side according to the plurality of function abnormal coefficients;
the calculation formula of the user side abnormal coefficient is as follows:
Wherein D ycx is an anomaly coefficient of the user terminal, e is a total number of live broadcast function types of the user terminal, G nyxa is a function anomaly coefficient of the a-th live broadcast function type, G ysa is a preset function threshold value of the a-th live broadcast function type, Q ga is a weight coefficient of the a-th live broadcast function type, and the weight coefficient has a value range of 0-1 and does not include 0.
Comparing each user side abnormal coefficient with other user side abnormal coefficients respectively to obtain a user side abnormal comparison result;
and determining an abnormal user terminal according to the user terminal comparison result.
When the abnormal coefficient of the user terminal is larger than the average value of all other abnormal coefficients of the user terminal, judging that the monitoring target user terminal corresponding to the abnormal coefficient of the user terminal is an abnormal user terminal;
and when the abnormal coefficient of the user terminal is smaller than or equal to the average value of all other abnormal coefficients of the user terminal, judging that the monitoring target user terminal corresponding to the abnormal coefficient of the user terminal is a risk user terminal.
The working principle of the technical scheme is as follows: in the previous step, the system has performed data collection and determination on each monitored indicator, determining which indicators are abnormal indicators. In this step, the system further obtains the monitoring data of the anomaly indexes for subsequent analysis. For each monitoring target user end (including a main broadcasting end and a viewer end needing to pay attention under specific conditions), the system calculates the function abnormality coefficient for each live broadcast function type according to the monitoring data of the abnormality index. This coefficient is usually a quantized value obtained by comprehensively evaluating a plurality of abnormality indexes, and is used for reflecting the abnormality degree of the user terminal on a certain function. And the system gathers or weighted averages all the abnormal function coefficients of each monitoring target user side, and calculates the abnormal function coefficients of the user side of each user side. The coefficient is used for quantitatively evaluating the overall abnormal condition of the user terminal and is used for subsequent comparison and judgment of the user terminal. The system compares the user terminal abnormality coefficient of each user terminal with the user terminal abnormality coefficients of all other user terminals to obtain a user terminal abnormality comparison result. This comparison process aims at finding clients that are behaving abnormally in the overall user population. According to the user side abnormality comparison result, the system adopts a certain judgment logic to determine the abnormal user side and the risk user side. Specifically, when the abnormal coefficient of the user terminal is larger than the average value of all other abnormal coefficients of the user terminal, the user terminal is judged to be an abnormal user terminal, and the abnormal condition is obvious; when the anomaly coefficient of the user terminal is smaller than or equal to the average value, the user terminal is judged to be a risk user terminal, which indicates that although the user terminal does not show obvious anomaly, a certain potential risk or instability factor exists.
The technical effects of the technical scheme are as follows: by calculating the function abnormality coefficient and the user side abnormality coefficient, the system can carry out fine management and evaluation on each user side, and timely discover and process the potential problem user side. The calculation formula of the functional abnormality coefficient G nyx realizes the fine monitoring and evaluation of the running state of each function of the live broadcast system by considering a plurality of indexes (such as data acquisition amount, acquisition abnormal data amount, index data total amount measurement and the like) of each live broadcast function type. The refined mode is helpful for rapidly positioning the problem source and reducing false alarm and missing report. The formula incorporates a weight factor Q i with a value in the range of 0 to 1 (excluding 0), which allows the differentiation of the importance of different indices. The index of higher importance is given a greater weight, and thus takes a more important place in the calculation of the functional abnormality coefficient. This flexibility helps to more accurately reflect the true state of the system. Because the formula is calculated based on the data acquired in real time, the formula can reflect the current abnormal function condition of the system in real time. Meanwhile, along with the change of the running state of the system (such as network fluctuation, equipment failure and the like), the collected data of each index also changes correspondingly, so that the dynamic update of the abnormal function coefficient is caused. This real-time and dynamic nature helps to discover and address potential problems in a timely manner. The abnormal coefficients of the user end can be further calculated by summarizing and comparing the abnormal coefficients of the function of each monitoring target user end. The result is helpful to identify the abnormal user side and the risk user side, thereby realizing intelligent allocation of network redundant operation resources. For the abnormal user side and the risk user side, the system can allocate more resources preferentially to ensure the stable operation of the system; for normal user end, the resource allocation can be reduced appropriately to increase the overall resource utilization. The calculation of the abnormal function coefficient and subsequent resource allocation measures are beneficial to improving the overall stability and user experience of the live broadcast system. By timely finding and solving the potential abnormal function problem, the system can maintain a continuous and stable running state; meanwhile, by optimizing the resource allocation strategy, the system can better meet the diversified demands of users and promote the satisfaction and loyalty of the users. The calculation formula of the anomaly coefficient of the user side provides a systematic method for comprehensively evaluating the comprehensive anomaly condition of the user side in different functional dimensions, and the one-sided performance of single index evaluation is avoided. Through the setting of the preset function threshold and the weight coefficient, a system administrator can flexibly adjust the evaluation standard according to actual requirements so as to adapt to different business scenes and evaluation requirements. By combining the data acquired in real time and a rapid calculation process, the formula can realize real-time monitoring and evaluation of the abnormal condition of the user side, and provides powerful support for timely response and processing. Based on the calculation result of the user side abnormal coefficient, the system can provide support for subsequent decisions, such as resource allocation, fault detection, performance optimization and the like. Compared with the monitoring of a single index, the comprehensive consideration of a plurality of functional abnormality coefficients can reflect the overall abnormality of the user terminal more accurately, and the accuracy and precision of problem identification are improved. The system can reasonably allocate resources to perform key monitoring and processing on the abnormal user side based on the comparison result of the abnormal coefficients of the user side, and avoid wasting the resources on the user side with normal performance. The abnormal user side can be found and processed in time, faults and interruption phenomena in the live broadcast process can be reduced, and the watching experience and satisfaction of the user are improved. Through a continuous monitoring and anomaly judging mechanism, the system can timely discover and process potential system faults and potential safety hazards, and stability and reliability of the whole live broadcast system are enhanced. The large amount of monitoring data and abnormal judgment results generated by the system provide precious data support for enterprises, and are helpful for the enterprises to make more scientific and reasonable decisions and plans based on the data.
In one embodiment of the present invention, the S4 includes:
Acquiring network redundant operation resources of a live broadcasting room, and averagely distributing 2/3 of the network redundant operation resources to all abnormal user terminals;
and uniformly distributing 1/3 of the network redundant operation resources to the risk user side for maintaining the functional health of the user side.
The working principle of the technical scheme is as follows: the system first identifies and obtains network redundancy operating resources currently available to the live broadcast room. These resources refer to spare network resources, such as additional bandwidth, server processing power, etc., that are additionally configured in the network architecture to handle bursty traffic or failure conditions. And according to the abnormal judgment result of the user end, the system formulates a resource allocation strategy. The core of the strategy is to divide the network redundant operation resource into two parts which are respectively used for supporting the abnormal user end and the risk user end. The system distributes the obtained 2/3 part of the network redundant operation resources evenly, so that each device judged to be an abnormal user side can obtain a certain proportion of additional resources. The resources are mainly used for relieving network pressure, improving data transmission stability and the like of an abnormal user terminal. The remaining 1/3 network redundant operating resources are then evenly distributed to all devices that are determined to be at risk clients. While these clients currently appear normal or only slightly abnormal, the allocation of additional resources helps to prevent potential problems from occurring and to maintain the health and stability of the client functions. The system can dynamically adjust the resource allocation strategy according to the real-time state of the user side and the network load condition. For example, when the problem of an abnormal ue is solved and normal is restored, the extra resources occupied by the abnormal ue may be recovered and reassigned to other ues needing support.
The technical effects of the technical scheme are as follows: by distributing additional resources for the abnormal user terminal, the system can rapidly relieve the network problem faced by the abnormal user terminal, and reduce live broadcast interruption and blocking phenomena, thereby improving the viewing experience of the user. The resource allocation mechanism helps to prevent single point failures from affecting the overall live system. By providing additional support for the risk user side, the system can prevent potential problems in advance and enhance the overall stability of the system. The system dynamically adjusts the resource allocation strategy according to the real-time state of the user side, and ensures that the resources are utilized to the maximum extent. This flexible way of resource management helps to reduce operating costs and improve system efficiency. When the user terminal is abnormal, the system can rapidly identify and allocate additional resources for the user terminal. The rapid response capability is beneficial to reducing the influence time of faults on the user side and improving the overall response speed of the system. In a high concurrency scene, reasonable allocation of network redundancy operation resources is beneficial to relieving the pressure of a server and a network, and smooth running of live broadcast service is ensured. This is particularly important for live platforms that support large-scale users online at the same time.
In one embodiment of the invention, the system comprises:
The monitoring target determining module is used for acquiring live broadcast data, determining a main broadcasting end and a spectator end according to the live broadcast data, monitoring the operation data of the main broadcasting end and the spectator end in real time, and determining a monitoring target user end according to the acquired user end monitoring data;
The index monitoring analysis module is used for carrying out index division on the acquired live broadcast function types to obtain monitoring indexes, collecting index monitoring data, comparing the index monitoring data with a preset index threshold value, and judging abnormal indexes and normal indexes according to comparison results;
the user side monitoring analysis module is used for calculating the function abnormality coefficient of each monitoring target user side, calculating the user side abnormality coefficient according to the function abnormality coefficient, comparing the user side abnormality coefficients with each other, and determining an abnormal user side and a risk user side according to the comparison result;
The resource allocation module is used for allocating network redundancy operation resources to the abnormal user side and the risk user side to obtain an allocation result.
The working principle of the technical scheme is as follows: the system firstly acquires initial data of live broadcast and on-demand, including basic information, on-demand time, anchor information and the like of the live broadcast. From this data, the system determines the anchor side (i.e., the provider of the live content) and the viewer side (i.e., the recipient of the live content). The system monitors the running data of the anchor side and the audience side in real time, including but not limited to network status, device performance, live content quality and the like. The monitoring data are collected by means of sensors, API interfaces and the like, and are subjected to preliminary processing and analysis. According to the obtained monitoring data of the user terminal, the system screens out monitoring target user terminals needing to be concerned, and the user terminals possibly become potential risk points due to network problems, insufficient equipment performance and the like. The system classifies the types of live broadcast functions, such as audio and video quality, interactive functions, payment functions and the like. For each function, corresponding monitoring indexes such as a click-through rate, delay time, interaction response speed and the like are set. The system collects real-time data of each monitoring index and compares the real-time data with a preset index threshold. Based on the comparison result, it is determined which indexes are abnormal (exceeding the threshold) or normal (within the threshold). And calculating the function abnormality coefficient of each monitoring target user terminal, wherein the coefficient is comprehensively obtained based on the abnormality degrees of a plurality of monitoring indexes. And further calculating the user side abnormal coefficients according to the functional abnormal coefficients, and comparing the user side abnormal coefficients with each other. According to the comparison result, determining which clients are abnormal clients (obvious problems exist directly) and risk clients (problems possibly occur in the future). And allocating network redundancy operation resources, such as bandwidth increase, route optimization, standby server starting and the like, to the abnormal user side and the risk user side. The distribution result aims to alleviate or eliminate the problems existing at the user side and improve the live broadcasting stability and user experience.
The technical effects of the technical scheme are as follows: the real-time monitoring and quick response mechanism is beneficial to timely finding and solving various abnormal problems in the live broadcast process, such as network jamming, equipment faults and the like, so that the live broadcast stability is improved. By optimizing network resources and equipment performance of the audience terminal, delay and blocking phenomena are reduced, and viewing experience of users is improved. By accurately identifying the abnormal and risk user terminals, reasonable distribution and efficient utilization of network resources are realized, and unnecessary operation cost is reduced. The large amount of monitoring data collected by the system provides powerful support for subsequent decisions, and is helpful for making more scientific and reasonable live broadcasting strategies and popularization plans.
The method has good expandability and flexibility, can be continuously upgraded and perfected along with the development of live broadcast service, and meets more demands in the future.
In one embodiment of the present invention, the monitoring target determining module includes:
Acquiring live broadcast data, and acquiring user side information of a live broadcast room according to the live broadcast data;
acquiring a video connection user end and a voice connection user end only from live broadcasting room user end information, wherein the video connection user end and the voice connection user end only are anchor ends;
The user end except the anchor end is a spectator end;
And monitoring the operation data of the video connection user terminal and the voice connection user terminal in real time to obtain the user terminal monitoring data of the video connection user terminal and the voice connection user terminal only and determine a monitoring target user terminal. The current online user end which is in communication connection is the monitoring target user end, and the disconnected user end is removed from the monitoring target user end.
The working principle of the technical scheme is as follows: the system is started first, and is connected to a data source of the live broadcast service to acquire initial data of the live broadcast. Such data may include the ID of the live broadcast, the time of play, the unique identification of the live broadcast room, etc. Based on the live broadcast data, the system further queries or subscribes to the real-time user side information of the live broadcast room. Such information typically includes the type of user side (e.g., anchor, audience), device information, network status, etc. In the live broadcasting room user side information, the system identifies a video connection user side and a voice connection user side only. Both types of clients are categorized as anchor because they directly contribute to the live content (i.e., live). After all the anchor terminals are removed, the rest of the user terminals are automatically classified as audience terminals. These clients mainly receive live content, but do not directly affect the live content. The system monitors the running data of the video connection user end and the voice connection user end in real time. The monitored data may include network latency, bandwidth occupancy, device performance (e.g., CPU, memory usage), audio video quality, etc. Based on the real-time monitored data, the system determines which anchor is currently online and in active communication connection. These clients are considered monitoring target clients because their status directly affects the stability and quality of the live broadcast. Once a particular anchor is disconnected or communication is interrupted, the system immediately removes it from the monitoring target client list to avoid unnecessary resource consumption and error monitoring.
The technical effects of the technical scheme are as follows: through monitoring the real-time operation data of the anchor, the system can timely discover and process potential problems which possibly affect the live broadcasting stability, such as network fluctuation, insufficient equipment performance and the like, so that the overall stability of live broadcasting is improved. Monitoring the running state of the anchor end is beneficial to ensuring the continuity and high-quality transmission of live broadcast content, reducing the problems of blocking, delay and the like possibly encountered by the audience end, and improving the watching experience of users. The system dynamically adjusts the monitoring target according to the currently online and communication connection anchor terminal, avoids invalid monitoring of disconnected or invalid user terminals, and improves the utilization efficiency of system resources. The real-time monitoring mechanism enables the system to rapidly respond to the change and abnormal conditions of the anchor end, rapidly locate the problem, take corresponding solving measures and reduce the influence of faults on live broadcasting. The clear host-side and audience-side classification and real-time monitoring mechanism make the maintenance and management of the system simpler and more efficient. The administrator can intuitively understand the real-time condition of the live broadcasting room and make necessary adjustment and optimization accordingly.
In one embodiment of the present invention, the index monitoring and analyzing module includes:
Acquiring live broadcast function types, and dividing indexes of each live broadcast function type according to preset index types to acquire a plurality of monitoring indexes of the live broadcast function types;
Collecting index data of each monitoring index of each anchor end and each audience end to obtain index monitoring data;
comparing the index monitoring data with a preset index threshold value to obtain an index comparison result;
And judging the normal index or the abnormal index of the corresponding monitoring index according to the index comparison result to obtain an index judgment result.
When the index monitoring data is larger than a preset index threshold value, judging the corresponding monitoring index as an abnormal index;
and when the index monitoring data is smaller than or equal to a preset index threshold value, judging the corresponding monitoring index as a normal index.
The working principle of the technical scheme is as follows: the system first identifies all live function categories available on the live platform, such as audio and video transmission, barrage interaction, gift presentation, wheat-over interaction, etc. For each live broadcast function type, the system performs detailed index division according to a preset index type. The index categories may include performance index (such as delay, packet loss rate), quality index (such as audio/video definition, synchronicity), user behavior index (such as interaction frequency, gift gifting amount), etc. Through the above division, the system obtains a plurality of specific monitoring indexes for each live function category. The system performs real-time or periodic data acquisition on each monitoring index of each anchor side and each audience side. The data collection means may include reading the server log directly, retrieving data through an API interface, deploying a monitoring probe at the client, etc. And after preliminary processing and cleaning, the acquired data form index monitoring data. The system compares the collected index monitoring data with a preset index threshold. The thresholds are preset according to service requirements and system performance requirements and are used for judging whether the monitoring index is in a normal range or not. The comparison result is divided into two cases: when the index monitoring data is larger than a preset index threshold value, the system judges the monitoring index as an abnormal index; and when the index monitoring data is smaller than or equal to a preset index threshold value, judging the index to be a normal index. The judging result is used for subsequent scenes such as exception handling, performance optimization, user experience improvement and the like.
The technical effects of the technical scheme are as follows: by dividing detailed indexes of the live broadcast function types and collecting real-time monitoring data, the system can accurately monitor various performance indexes and quality indexes in the live broadcast process, so that the system can quickly respond and solve potential problems. Through timely discovery and processing of abnormal indexes, the system can optimize the quality of live audio and video, reduce the problems of delay, packet loss and the like, and promote the viewing experience of users. Monitoring the user behavior index is helpful for the system to know the preference and the demand of the user, so that more personalized and accurate service is provided, and the user experience is further improved. The real-time monitoring and abnormality judging mechanism is beneficial to timely finding and processing potential system faults and potential safety hazards by the system, and stability and reliability of the system are enhanced. The large amount of monitoring data collected by the system provides powerful support for subsequent decisions. Through data analysis, enterprises can know information such as development conditions of live broadcast services, user behavior characteristics and the like, so that more scientific and reasonable service strategies and development planning are formulated. The whole monitoring and judging process realizes automation and intellectualization, reduces the possibility of manual intervention and erroneous judgment, and improves the working efficiency and accuracy.
In one embodiment of the present invention, the client monitoring and analyzing module includes:
acquiring index monitoring data of abnormal indexes according to the index judgment result, and calculating the functional abnormality coefficient of each live broadcast functional category of each monitoring target user side according to the index monitoring data of each monitoring target user side;
The calculation formula of the functional abnormality coefficient is as follows:
wherein G nyx is a functional abnormality coefficient, j is the total number of indexes of the live broadcast functional category, L yci is the acquired abnormal data volume of the ith index, L cji is the data acquisition volume of the ith index, L zbi is the index data total amount measurement of the ith index, Q i is the weight coefficient of the ith index, and the weight coefficient has a value range of 0-1 and does not comprise 0.
Calculating a user side abnormal coefficient of each monitoring target user side according to the plurality of function abnormal coefficients;
the calculation formula of the user side abnormal coefficient is as follows:
Wherein D ycx is an anomaly coefficient of the user terminal, e is a total number of live broadcast function types of the user terminal, G nyxa is a function anomaly coefficient of the a-th live broadcast function type, G ysa is a preset function threshold value of the a-th live broadcast function type, Q ga is a weight coefficient of the a-th live broadcast function type, and the weight coefficient has a value range of 0-1 and does not include 0.
Comparing each user side abnormal coefficient with other user side abnormal coefficients respectively to obtain a user side abnormal comparison result;
and determining an abnormal user terminal according to the user terminal comparison result.
When the abnormal coefficient of the user terminal is larger than the average value of all other abnormal coefficients of the user terminal, judging that the monitoring target user terminal corresponding to the abnormal coefficient of the user terminal is an abnormal user terminal;
and when the abnormal coefficient of the user terminal is smaller than or equal to the average value of all other abnormal coefficients of the user terminal, judging that the monitoring target user terminal corresponding to the abnormal coefficient of the user terminal is a risk user terminal.
The working principle of the technical scheme is as follows: in the previous step, the system has performed data collection and determination on each monitored indicator, determining which indicators are abnormal indicators. In this step, the system further obtains the monitoring data of the anomaly indexes for subsequent analysis. For each monitoring target user end (including a main broadcasting end and a viewer end needing to pay attention under specific conditions), the system calculates the function abnormality coefficient for each live broadcast function type according to the monitoring data of the abnormality index. This coefficient is usually a quantized value obtained by comprehensively evaluating a plurality of abnormality indexes, and is used for reflecting the abnormality degree of the user terminal on a certain function. And the system gathers or weighted averages all the abnormal function coefficients of each monitoring target user side, and calculates the abnormal function coefficients of the user side of each user side. The coefficient is used for quantitatively evaluating the overall abnormal condition of the user terminal and is used for subsequent comparison and judgment of the user terminal. The system compares the user terminal abnormality coefficient of each user terminal with the user terminal abnormality coefficients of all other user terminals to obtain a user terminal abnormality comparison result. This comparison process aims at finding clients that are behaving abnormally in the overall user population. According to the user side abnormality comparison result, the system adopts a certain judgment logic to determine the abnormal user side and the risk user side. Specifically, when the abnormal coefficient of the user terminal is larger than the average value of all other abnormal coefficients of the user terminal, the user terminal is judged to be an abnormal user terminal, and the abnormal condition is obvious; when the anomaly coefficient of the user terminal is smaller than or equal to the average value, the user terminal is judged to be a risk user terminal, which indicates that although the user terminal does not show obvious anomaly, a certain potential risk or instability factor exists.
The technical effects of the technical scheme are as follows: by calculating the function abnormality coefficient and the user side abnormality coefficient, the system can carry out fine management and evaluation on each user side, and timely discover and process the potential problem user side. Compared with the monitoring of a single index, the comprehensive consideration of a plurality of functional abnormality coefficients can reflect the overall abnormality of the user terminal more accurately, and the accuracy and precision of problem identification are improved. The system can reasonably allocate resources to perform key monitoring and processing on the abnormal user side based on the comparison result of the abnormal coefficients of the user side, and avoid wasting the resources on the user side with normal performance. The abnormal user side can be found and processed in time, faults and interruption phenomena in the live broadcast process can be reduced, and the watching experience and satisfaction of the user are improved. Through a continuous monitoring and anomaly judging mechanism, the system can timely discover and process potential system faults and potential safety hazards, and stability and reliability of the whole live broadcast system are enhanced. The large amount of monitoring data and abnormal judgment results generated by the system provide precious data support for enterprises, and are helpful for the enterprises to make more scientific and reasonable decisions and plans based on the data.
In one embodiment of the present invention, the resource allocation module includes:
Acquiring network redundant operation resources of a live broadcasting room, and averagely distributing 2/3 of the network redundant operation resources to all abnormal user terminals;
and uniformly distributing 1/3 of the network redundant operation resources to the risk user side for maintaining the functional health of the user side.
The working principle of the technical scheme is as follows: the system first identifies and obtains network redundancy operating resources currently available to the live broadcast room. These resources refer to spare network resources, such as additional bandwidth, server processing power, etc., that are additionally configured in the network architecture to handle bursty traffic or failure conditions. And according to the abnormal judgment result of the user end, the system formulates a resource allocation strategy. The core of the strategy is to divide the network redundant operation resource into two parts which are respectively used for supporting the abnormal user end and the risk user end. The system distributes the obtained 2/3 part of the network redundant operation resources evenly, so that each device judged to be an abnormal user side can obtain a certain proportion of additional resources. The resources are mainly used for relieving network pressure, improving data transmission stability and the like of an abnormal user terminal. The remaining 1/3 network redundant operating resources are then evenly distributed to all devices that are determined to be at risk clients. While these clients currently appear normal or only slightly abnormal, the allocation of additional resources helps to prevent potential problems from occurring and to maintain the health and stability of the client functions. The system can dynamically adjust the resource allocation strategy according to the real-time state of the user side and the network load condition. For example, when the problem of an abnormal ue is solved and normal is restored, the extra resources occupied by the abnormal ue may be recovered and reassigned to other ues needing support.
The technical effects of the technical scheme are as follows: by distributing additional resources for the abnormal user terminal, the system can rapidly relieve the network problem faced by the abnormal user terminal, and reduce live broadcast interruption and blocking phenomena, thereby improving the viewing experience of the user. The resource allocation mechanism helps to prevent single point failures from affecting the overall live system. By providing additional support for the risk user side, the system can prevent potential problems in advance and enhance the overall stability of the system. The system dynamically adjusts the resource allocation strategy according to the real-time state of the user side, and ensures that the resources are utilized to the maximum extent. This flexible way of resource management helps to reduce operating costs and improve system efficiency. When the user terminal is abnormal, the system can rapidly identify and allocate additional resources for the user terminal. The rapid response capability is beneficial to reducing the influence time of faults on the user side and improving the overall response speed of the system. In a high concurrency scene, reasonable allocation of network redundancy operation resources is beneficial to relieving the pressure of a server and a network, and smooth running of live broadcast service is ensured. This is particularly important for live platforms that support large-scale users online at the same time.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
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